This invention relates generally to the field of optical lithography, and more particularly, to a method for verifying the accuracy of the results of an Model Based Optical Proximity Correction (MBOPC) software tool for use in an optical lithography simulation, to provide accurate correction of the device shapes in a photo-mask that fulfill required performance criteria for the resulting Very Large Scale Integrated (VLSI) circuit.
The optical micro-lithography process in semiconductor fabrication, also known as the photolithography process, consists of duplicating desired circuit patterns onto semiconductor wafers for an overall desired circuit performance. The desired circuit patterns are typically represented as opaque, complete and semi-transparent regions on a template commonly referred to as a photomask. In optical micro-lithography, patterns on the photo-mask template are projected onto the photo-resist coated wafer by way of optical imaging through an exposure system.
The continuous advancement of VLSI chip manufacturing technology to meet Moore's law of shrinking device dimensions in geometric progression has spurred the development of Resolution Enhancement Techniques (RET) and Optical Proximity Correction (OPC) methodologies in optical microlithography. The latter is the method of choice for chip manufacturers for the foreseeable future due to its high volume yield in manufacturing and past history of success. However, the ever shrinking device dimensions combined with the desire to enhance circuit performance in the deep sub-wavelength domain require complex OPC methodologies to ensure the fidelity of mask patterns on the printed wafer.
The ever increasing cost of mask manufacturing and inspection and the ever increasing complexity of OPC and RET require that the mask is correctly and accurately simulated for potential defects before the mask is manufactured. This area is generally known as Mask Manufacturability Verification or printability verification. Accurate simulation is the primary focus of Printability Verification. This means that the Printability Verification simulation should not miss any real error on the mask. The cost of finding the error when the mask is actually manufactured and is being used for chip manufacturing is extremely high. Nevertheless, there are two other equally important objectives of a Printability Verification tool. First it needs to be done as quickly as possible. The feedback from the Printability Verification is used for development of OPC and RET. A fast feedback is useful to minimize the Turn around Time (TAT) for OPC and RET developments. Second there should be as few false errors as possible. A false error is defined as error identified by Printability Verification using its simulation tool, which does not happen on the wafer. Since, a missed error is significantly more expensive than a false error, all printability verification tools are expected to err on the conservative side. However, since each error, whether false or real needs to be checked manually, it is important that there are not too many false errors either. If there are too many false errors, the real errors may be missed by the manual inspection and also it would require a lot of time to shift through all the false errors to find the real errors.
Optical models used in printability verification are typically the same optical models that may be used in model-based optical proximity correction (MBOPC). These approaches are in one form or another, related to the Sum of Coherent Source (SOCS) method, which is an algorithm for efficient calculation of the bilinear transform of the Hopkins integral, to be described in more detail below. The SOCS approximation of the Hopkins integral involves convolution of Hopkins kernels (eigenfunctions).
Current printability verification methods tend to simulate the whole mask layout image with the most accurate model using very conservative criteria. This tends to increase the runtime of the printability verification along with the number of false errors.
In current art, printability verification is done with the largest number of Hopkins kernels that are computationally possible. The larger the number of the kernels used, the greater the accuracy of the resulting simulated image. On the other hand with as the number of kernels used increases, the runtime performance for printability verification gets slowed down considerably. The same simulation accuracy is used over the whole layout irrespective of whether there is any error or not. Typically, over the majority of the layout, there are no printability errors. Furthermore, the goal of printability verification is to determine the existence of errors in printability, and identify such errors, rather than accurately determining what the image will look like. However, it is also important that any real printability errors are not missed as a result of less accurate simulations.
In view of the above, there is a need for a fast method of mask printability verification that is efficient in identifying printability errors without missing any such errors.